
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowercase    :   List[Any]						=			logging.get_logger(__name__)




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Optional[Any])   ->       Dict:






							'''simple docstring'''





							__UpperCamelCase      :  Optional[int]       =   OrderedDict()
							for key, value in state_dict.items():
														if key.startswith("module.encoder"):
																					__UpperCamelCase      :  List[str]       =   key.replace("module.encoder"		,    "glpn.encoder")
														if key.startswith("module.decoder"):
																					__UpperCamelCase      :  Optional[int]       =   key.replace("module.decoder"		,    "decoder.stages")
														if "patch_embed" in key:
																					# replace for example patch_embed1 by patch_embeddings.0
																					__UpperCamelCase      :  int       =   key[key.find("patch_embed") + len("patch_embed")]
																					__UpperCamelCase      :  Optional[Any]       =   key.replace(F'patch_embed{idx}'		,    F'patch_embeddings.{int(_lowerCamelCase)-1}')
														if "norm" in key:
																					__UpperCamelCase      :  int       =   key.replace("norm"		,    "layer_norm")
														if "glpn.encoder.layer_norm" in key:
																					# replace for example layer_norm1 by layer_norm.0
																					__UpperCamelCase      :  str       =   key[key.find("glpn.encoder.layer_norm") + len("glpn.encoder.layer_norm")]
																					__UpperCamelCase      :  Any       =   key.replace(F'layer_norm{idx}'		,    F'layer_norm.{int(_lowerCamelCase)-1}')
														if "layer_norm1" in key:
																					__UpperCamelCase      :  Union[str, Any]       =   key.replace("layer_norm1"		,    "layer_norm_1")
														if "layer_norm2" in key:
																					__UpperCamelCase      :  Any       =   key.replace("layer_norm2"		,    "layer_norm_2")
														if "block" in key:
																					# replace for example block1 by block.0
																					__UpperCamelCase      :  List[str]       =   key[key.find("block") + len("block")]
																					__UpperCamelCase      :  Union[str, Any]       =   key.replace(F'block{idx}'		,    F'block.{int(_lowerCamelCase)-1}')
														if "attn.q" in key:
																					__UpperCamelCase      :  int       =   key.replace("attn.q"		,    "attention.self.query")
														if "attn.proj" in key:
																					__UpperCamelCase      :  Dict       =   key.replace("attn.proj"		,    "attention.output.dense")
														if "attn" in key:
																					__UpperCamelCase      :  List[str]       =   key.replace("attn"		,    "attention.self")
														if "fc1" in key:
																					__UpperCamelCase      :  Dict       =   key.replace("fc1"		,    "dense1")
														if "fc2" in key:
																					__UpperCamelCase      :  int       =   key.replace("fc2"		,    "dense2")
														if "linear_pred" in key:
																					__UpperCamelCase      :  Dict       =   key.replace("linear_pred"		,    "classifier")
														if "linear_fuse" in key:
																					__UpperCamelCase      :  str       =   key.replace("linear_fuse.conv"		,    "linear_fuse")
																					__UpperCamelCase      :  Optional[Any]       =   key.replace("linear_fuse.bn"		,    "batch_norm")
														if "linear_c" in key:
																					# replace for example linear_c4 by linear_c.3
																					__UpperCamelCase      :  Union[str, Any]       =   key[key.find("linear_c") + len("linear_c")]
																					__UpperCamelCase      :  List[str]       =   key.replace(F'linear_c{idx}'		,    F'linear_c.{int(_lowerCamelCase)-1}')
														if "bot_conv" in key:
																					__UpperCamelCase      :  Tuple       =   key.replace("bot_conv"		,    "0.convolution")
														if "skip_conv1" in key:
																					__UpperCamelCase      :  Any       =   key.replace("skip_conv1"		,    "1.convolution")
														if "skip_conv2" in key:
																					__UpperCamelCase      :  Union[str, Any]       =   key.replace("skip_conv2"		,    "2.convolution")
														if "fusion1" in key:
																					__UpperCamelCase      :  Optional[Any]       =   key.replace("fusion1"		,    "1.fusion")
														if "fusion2" in key:
																					__UpperCamelCase      :  Optional[Any]       =   key.replace("fusion2"		,    "2.fusion")
														if "fusion3" in key:
																					__UpperCamelCase      :  Dict       =   key.replace("fusion3"		,    "3.fusion")
														if "fusion" in key and "conv" in key:
																					__UpperCamelCase      :  Union[str, Any]       =   key.replace("conv"		,    "convolutional_layer")
														if key.startswith("module.last_layer_depth"):
																					__UpperCamelCase      :  Optional[Any]       =   key.replace("module.last_layer_depth"		,    "head.head")
														__UpperCamelCase      :  int       =   value

							return new_state_dict




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     List[str])   ->       Union[str, Any]:






							'''simple docstring'''





							for i in range(config.num_encoder_blocks):
														for j in range(config.depths[i]):
																					# read in weights + bias of keys and values (which is a single matrix in the original implementation)
																					__UpperCamelCase      :  List[str]       =   state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight')
																					__UpperCamelCase      :  Optional[int]       =   state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias')
																					# next, add keys and values (in that order) to the state dict
																					__UpperCamelCase      :  List[str]       =   kv_weight[
																					    : config.hidden_sizes[i], :
																					]
																					__UpperCamelCase      :  int       =   kv_bias[: config.hidden_sizes[i]]
																					__UpperCamelCase      :  Tuple       =   kv_weight[
																					    config.hidden_sizes[i] :, :
																					]
																					__UpperCamelCase      :  List[Any]       =   kv_bias[config.hidden_sizes[i] :]




def       _SCREAMING_SNAKE_CASE   (						)   ->       Optional[int]:






							'''simple docstring'''





							__UpperCamelCase      :  Any       =   "http://images.cocodataset.org/val2017/000000039769.jpg"
							__UpperCamelCase      :  Tuple       =   Image.open(requests.get(_lowerCamelCase		,    stream=_lowerCamelCase).raw)

							return image




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     str		,    _lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     Dict=False		,    _lowerCamelCase					:     Any=None)   ->       Any:






							'''simple docstring'''





							__UpperCamelCase      :  Dict       =   GLPNConfig(hidden_sizes=[64, 128, 320, 512]		,    decoder_hidden_size=64		,    depths=[3, 8, 27, 3])

							# load image processor (only resize + rescale)
							__UpperCamelCase      :  List[str]       =   GLPNImageProcessor()

							# prepare image
							__UpperCamelCase      :  Dict       =   prepare_img()
							__UpperCamelCase      :  List[Any]       =   image_processor(images=_lowerCamelCase		,    return_tensors="pt").pixel_values

							logger.info("Converting model...")

							# load original state dict
							__UpperCamelCase      :  Tuple       =   torch.load(_lowerCamelCase		,    map_location=torch.device("cpu"))

							# rename keys
							__UpperCamelCase      :  Tuple       =   rename_keys(_lowerCamelCase)

							# key and value matrices need special treatment
							read_in_k_v(_lowerCamelCase		,    _lowerCamelCase)

							# create HuggingFace model and load state dict
							__UpperCamelCase      :  Tuple       =   GLPNForDepthEstimation(_lowerCamelCase)
							model.load_state_dict(_lowerCamelCase)
							model.eval()

							# forward pass
							__UpperCamelCase      :  Optional[int]       =   model(_lowerCamelCase)
							__UpperCamelCase      :  Union[str, Any]       =   outputs.predicted_depth

							# verify output
							if model_name is not None:
														if "nyu" in model_name:
																					__UpperCamelCase      :  Union[str, Any]       =   torch.tensor(
																					    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]])
														elif "kitti" in model_name:
																					__UpperCamelCase      :  Optional[Any]       =   torch.tensor(
																					    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]])
														else:
																					raise ValueError(F'Unknown model name: {model_name}')

														__UpperCamelCase      :  str       =   torch.Size([1, 480, 640])

														assert predicted_depth.shape == expected_shape
														assert torch.allclose(predicted_depth[0, :3, :3]		,    _lowerCamelCase		,    atol=1e-4)
														print("Looks ok!")

							# finally, push to hub if required
							if push_to_hub:
														logger.info("Pushing model and image processor to the hub...")
														model.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase		,    _lowerCamelCase)		,    organization="nielsr"		,    commit_message="Add model"		,    use_temp_dir=_lowerCamelCase		,    )
														image_processor.push_to_hub(
														    repo_path_or_name=Path(_lowerCamelCase		,    _lowerCamelCase)		,    organization="nielsr"		,    commit_message="Add image processor"		,    use_temp_dir=_lowerCamelCase		,    )


if __name__ == "__main__":
		lowercase    :   Union[str, Any]						=			argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_path',
		    default=None,
		    type=str,
		    help='Path to the original PyTorch checkpoint (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
		)
		parser.add_argument(
		    '--model_name',
		    default='glpn-kitti',
		    type=str,
		    help='Name of the model in case you\'re pushing to the hub.',
		)
		lowercase    :   Dict						=			parser.parse_args()
		convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)

from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip